Bibliographic record
Abstract
Many solutions have been proposed to fight the problem of bias in AI. The paper arranges them into five categories: (a) "no gender or race" - ignoring and omitting any reference to gender and race from the dataset; (b) transparency - revealing the considerations that led the algorithm to reach a certain conclusion; (c) designing algorithms that are not biased; (d) "machine education" that complements "machine learning" by adding value sensitivity to the algorithm; or (e) involving humans in the process. The paper will selectively provide policy recommendations to promote the solutions of transparency (b) and human-in-the-loop (e). For transparency, the policy can be inspired by the measures implemented in the pharmaceutical industry for drug approval. To promote human-in-the-loop, the paper proposes an "ombudsman" mechanism that ensures the biases detected by the users are dealt with by the companies who develop and run the algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.039 | 0.029 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".